Autonomous navigation in complex environments featuring obstacles, varying ground compositions, and external disturbances requires real-time motion generation and stabilization simultaneously. In this paper, we present and evaluate a strategy for rejection of external disturbances and real-time motion generation in the presence of obstacles and non-flat ground. We propose different solutions for combining the associated algorithms and analyze them in simulations The promising method is validated in experiments with our robot Lola . We found a hierarchical approach to be effective for solving these complex motion generation problems, because it allows us to decompose the problem into sub-problems that can be tackled separately at different levels. This makes the approach suitable for real-time applications and robust against perturbations and errors. Our results show that real-time motion planning and disturbance rejection can be combined to improve the autonomy of legged robots.